Multi-Criteria Decision-Making Approach for Siting Sewer Treatment Plants in Muscat, Oman

Sewer Treatment Plants (STPs) are essential pieces of infrastructure given the growing scarcity of water sources due to the challenges of urbanization. The positioning of STPs is a complex multidimensional process that involves integrative decision-making approaches that consider multiple sustainability criteria to ensure their optimal placement. The Multi-Criteria Decision Method (MCDM) is a suite of approaches available to decision-makers when making systematic and scientifically informed decisions on siting wastewater treatment plants. Although MCDM methods have manifold applications in different geographic contexts, there is a paucity of studies employing MCDM models for the siting of STPs within the context of Oman. In this study, we assessed the locations of existing STPs and identified suitable locations for future STPs within the Muscat Governorate of Oman using a Multi-Criteria Decision-Making Analytic Hierarchy Process (MCDM-AHP) model in a Geographic Information System (GIS) environment. Eight factors were considered in the MCDM-AHP model: slope, elevation, proximity to built-up areas, airports, valleys, road networks, the sea, parks, and golf courses. Each factor was assigned priority weights based on its importance using the AHP method. Thematic maps were generated to categorize the potential sites into different suitability levels. The results showed that the coastal areas of A’Seeb and Bowsher were the most suitable locations for STPs, representing only 1.19% of the total study area. The novelty of this study stems from the perspective of an original application within the context of Oman, which has generated novel results and interpretations. This has significant implications for urban policy and planning with respect to better informing decision-makers with a systematic framework for efficient wastewater treatment

Effects of Spatial Characteristics on Non-Standard Employment for Canada’s Immigrant Population

Using microdata from Statistics Canada’s Labour Force Survey (LFS) and Population Census, this paper explores how spatial characteristics are correlated with temporary employment outcomes for Canada’s immigrant population. Results from ordinary least square regression models suggest that census metropolitan areas and census agglomerations (CMAs/CAs) characterized by a high share of racialized immigrants, immigrants in low-income, young, aged immigrants, unemployed immigrants, and immigrants employed in health and service occupations were positively associated with an increase in temporary employment for immigrants. Furthermore, findings from principal component regression models revealed that a combination of spatial characteristics, namely CMAs/CAs characterized by both a high share of unemployed immigrants and immigrants in poverty, had a greater likelihood of immigrants being employed temporarily. The significance of this study lies in the spatial conceptualization of temporary employment for immigrants that could better inform spatially targeted employment policies, especially in the wake of the structural shift in the nature of work brought about by the COVID-19 pandemic

COVID-19 Spatial Policy: A Comparative Review of Urban Policies in the European Union and the Middle East

This article reviews existing research and discussions on urban policy in response to the global COVID-19 pandemic. The analysis focuses on the first pandemic period in the European Union and the Middle East. A search was conducted in available databases and search engines (Scopus, Google Scholar). A critical bibliometric analysis of publications from the first pandemic period was carried out. The most frequent topics covered were spatial organization, transport, environment, and social issues. The analysis showed that the quantitative scope and depth of the selected topics in the European Union and the Middle East differ. Activities defined as “exploitative” should be considered a particularly interesting point of reference in both analyzed regions

Conversion of Industrial Sludge into Activated Biochar for Effective Cationic Dye Removal: Characterization and Adsorption Properties Assessment

This paper presents an in-depth characterization of a raw industrial sludge (IS-R) and its KOH-activated biochar pyrolyzed at 750 °C (IS-KOH-B) followed by their application to remove a cationic dye from aqueous solution. Materials characterization shows that compared to the IS-R, the IS-KOH-B has improved structural, textural, and surface chemical properties. In particular, the IS-KOH-B’s BET surface area and total pore volume are about 78 and 6 times higher than those found for the IS-R, respectively. The activated biochar efficiently retained the cationic dye under wide experimental conditions. Indeed, for an initial dye concentration of 50 mg L−1, removal yields were assessed to be more than 92.5%, 93.5%, and 97.8% for a large pH range (4–10), in the presence of high contents of competing cations (3000 mg L−1 of Ca2+, Mg2+, Na+, and K+), and a low used adsorbent dose (1 g L−1), respectively. The Langmuir’s adsorption capacities were 48.5 and 65.9 mg g−1 for of IS-R and IS-KOH-B, respectively, which are higher than those reported for various adsorbents in the literature. The dye removal was found to be monolayer, spontaneous, and endothermic for both the adsorbents. Moreover, this removal process seems to be controlled by chemical reactions for IS-KOH-B whereas by both physico–chemical reactions for IS-R. This study demonstrates that the raw industrial sludge and especially its KOH-activated derived biochar could be considered as promising adsorbents for the removal of dyes from aqueous solutions